Show HN: Agent framework that generates its own topology and evolves at runtime

AI that builds itself and learns from mistakes—commenters are buzzing

TLDR: Hive claims to create AI agents that design their own workflows and improve by learning from errors. Commenters raved about turning crashes into lessons, pitched ambitious use cases, and asked if this could finally make self-healing software practical for real businesses.

Show HN dropped a brain-bender: Hive promises AI agents that build their own wiring and keep improving while they run. The comments instantly stole the show. One dev nailed the vibe: “The hardest mental shift… treating Exceptions as Observations” — turning errors into clues the system feeds itself to get smarter. Another summed up the philosophy as a shift from hard‑coded workflows to result‑oriented thinking, aka stop babysitting steps and focus on outcomes. Fans loved the built‑in human‑in‑the‑loop controls, while the 7k stars and 4k forks screamed hype. It’s like giving your bot a to‑do list and a therapy session for its mistakes, then watching it redeploy a better version.

Curious onlookers chimed in with wild ideas. One wondered about an “eventually consistent engine” to sync documents across time, another pitched a goal‑driven webserver that collects telemetry and grades itself. Jokes rolled in about bots opening support tickets on themselves and error logs becoming self-help diaries. Even skeptics mostly kept it chill, dropping “watching closely” vibes. If Hive actually delivers self-healing, production‑grade agents, devs might finally ditch brittle flowcharts for adaptable, outcome‑hungry software.

Key Points

  • Hive generates a node graph and dynamic connection code from conversationally defined goals.
  • The framework captures failure data and evolves agents via a coding agent, then redeploys (self-healing).
  • It includes human-in-the-loop nodes, credential management, and real-time monitoring for production control.
  • Hive targets long-running, autonomous agents with strong guardrails and continuous improvement.
  • Prerequisites include Python 3.11+, optional Claude Code or Cursor; Windows users are advised to use WSL or Git Bash.

Hottest takes

"The hardest mental shift for us was treating Exceptions as Observations" — vincentjiang
"I am of course unqualified to provide useful commentary on it, but I find this concept to be new and interesting" — Multicomp
"What if you wrote something like a webserver that was given "goals" for a backend" — foota
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